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296MATHEMATICS

t . Statistician Karl Pearson spent some more time, making 24000 tosses of a coin. He got 12012 heads, and thus, the experimental probability of a head obtained by him was 0.5005. Now, suppose we ask, 'What will the experimental probability of a head be if the experiment is carried on upto, say, one million times? Or 10 million times? And so on?" You would intuitively feel that as the number of tosses increases, the experimental probability of a head (or a tail) seems to be settling down around the number 0.5 , i.e.,

15.2P.1robailP2tbtrar1y of getting a head (or getting a

tail), as you will see in the next section. In this chapter, we provide an introduction to the theoretical (also called classical) probability of an event, and discuss simple problems based on this concept.

15.2Probability - A Theoretical Approach

Let us consider the following situation :

Suppose a coin is tossed b1iPb -2A.

When we speak of a coin, we assume it to be 'fair", that is, it is symmetrical so that there is no reason for it to come down more often on one side than the other. We call this property of the coin as being 'unbiased". By the phrase 'random toss", we mean that the coin is allowed to fall freely without any trbT or r 1.Ph.P. o.. We know, in advance, that the coin can only land in one of two possible ways - either head up or tail up (we dismiss the possibility of its 'landing" on its edge, which may be possible, for example, if it falls on sand). We can reasonably assume that each outcome, head or tail, is bTiare.ayi12i2oocPibTi15.i215.Pp We refer to this by saying that

15.i2c1o2A.T head and tail, bP. .scbaayiare.ay.

PROBABILITY297

equally likely outcomes of throwing a die are 1, 2, 3, 4, 5 and 6. Are the outcomes of every experiment equally likely? Let us see. Suppose that a bag contains 4 red balls and 1 blue ball, and you draw a ball without looking into the bag. What are the outcomes? Are the outcomes - a red ball and a blue ball equally likely? Since there are 4 red balls and only one blue ball, you would agree that you are more likely to get a red ball than a blue ball. So, the outcomes (a red ball or a blue ball) are not equally likely. However, the outcome of drawing a ball of any colour from the bag is equally likely. So, all experiments do not necessarily have equally likely outcomes. However, in this chapter, from now on, we will assume that all the experiments have equally likely outcomes. In Class IX, we defined the experimental or empirical probability P(E) of an event E as

P(E) =

theoretical probability (also called classical probability) of an event E, written as P(E), is defined as

P(E) =

298MATHEMATICS

equally likely. We will briefly refer to theoretical probability as probability. This definition of probability was given by Pierre Simon Laplace in 1795. Probability theory had its origin in the 16th century when an Italian physician and mathematician J.Cardan wrote the first book on the subject,

The Book on Games of Chance.

Since its inception, the study of probability has attracted the attention of great mathematicians. James Bernoulli (1654 - 1705), A. de Moivre (1667 - 1754), and Pierre Simon Laplace are among those who made significant contributions to this field. Laplace"s Theorie Analytique des Probabilités, 1812, is considered to be the greatest contribution by a single person to the theory of probability. In recent years, probability has been used extensively in many areas such as biology, economics, genetics, physics, sociology etc. Let us find the probability for some of the events associated with experiments where the equally likely assumption holds. Example 1 : Find the probability of getting a head when a coin is tossed once. Also find the probability of getting a tail. Solution : In the experiment of tossing a coin once, the number of possible outcomes is two - Head (H) and Tail (T). Let E be the event 'getting a head". The number of outcomes favourable to E, (i.e., of getting a head) is 1. Therefore,

P(E) = P (head) =

Example 2 : A bag contains a red ball, a blue ball and a yellow ball, all the balls being of the same size. Kritika takes out a ball from the bag without looking into it. What is the probability that she takes out the (i)yellow ball?(ii)red ball?(iii)blue ball?

Pierre Simon Laplace

(1749 - 1827)

PROBABILITY299

cpTb cCn nKritika takes out a ball from the bag without looking into it. So, it is equally likely that she takes out any one of them. Let Y be the event 'the ball taken out is yellow", B be the event 'the ball taken out is blue", and R be the event 'the ball taken out is red".

Now, the number of possible outcomes = 3.

(i)The number of outcomes favourable to the event Y = 1.

So,P(Y) =

5 †kBliéqn

1.An event having only one outcome of the experiment is called an .a.A. 1bPy

.E. 1p In Example 1, both the events E and F are elementary events. Similarly, in Example 2, all the three events, Y, B and R are elementary events.

2.In Example 1, we note that : P(E) + P(F) = 1

In Example 2, we note that : P(Y) + P(R) + P(B) = 1 lCnkG.ki BkCbnis 1. This is true in general also. vGlB.pkn‡nnSuppose we throw a die once. (i) What is the probability of getting a number greater than 4 ? (ii) What is the probability of getting a number less than or equal to 4 ? cpTb cCnn(i) Here, let E be the event 'getting a number greater than 4". The number of possible outcomes is six : 1, 2, 3, 4, 5 and 6, and the outcomes favourable to E are 5 and 6. Therefore, the number of outcomes favourable to E is 2. So,

P(E) =P(number greater than 4) =

300MATHEMATICS

Ccb because the event E has 2 outcomes and the event F has 4 outcomes. †kBliéqn From Example 1, we note that

P(E) + P(F) =

.1 .1 2 4". Note that getting a number 21igreater than 4 is same as getting a number less than or equal to 4, and vice versa. In (1) and (2) above, is F not the same as 'not E"? Yes, it is. We denote the event 'not E" by r,

The event

ucB.pkBkCb of the event E.

We also say that E and

ucB.pkBkCbliy events. Before proceeding further, let us try to find the answers to the following questions: (i)What is the probability of getting a number 8 in a single throw of a die? (ii)What is the probability of getting a number less than 7 in a single throw of a die? kbnTqnlCqŠkin, xn We know that there are only six possible outcomes in a single throw of a die. These outcomes are 1, 2, 3, 4, 5 and 6. Since no face of the die is marked 8, so there is no outcome favourable to 8, i.e., the number of such outcomes is zero. In other words, getting 8 in a single throw of a die, is rAl2TTrta..

So,P(getting 8) =

PROBABILITY301

impossible to occur is 0. Such an event is called an impossible event.

Let us answer (ii) :

Since every face of a die is marked with a number less than 7, it is sure that we will always get a number less than 7 when it is thrown once. So, the number of favourable outcomes is the same as the number of all possible outcomes, which is 6.

Therefore,P(E) =P(getting a number less than 7) =

sure (or certain) to occur is 1. Such an event is called a sure event or a certain event. Note : From the definition of the probability P(E), we see that the numerator (number of outcomes favourable to the event E) is always less than or equal to the denominator (the number of all possible outcomes). Therefore, 0

22222 P(E) 22222 1

Now, let us take an example related to playing cards. Have you seen a deck of playing cards? It consists of 52 cards which are divided into 4 suits of 13 cards each- spades (?), hearts (?), diamonds (?) and clubs (?). Clubs and spades are of black colour, while hearts and diamonds are of red colour. The cards in each suit are ace, king, queen, jack, 10, 9, 8, 7, 6, 5, 4, 3 and 2. Kings, queens and jacks are called face cards.

Example 4 :

One card is drawn from a well-shuffled deck of 52 cards. Calculate the probability that the card will (i)be an ace, (ii)not be an ace. Solution : Well-shuffling ensures equally likely outcomes. (i)There are 4 aces in a deck. Let E be the event 'the card is an ace".

The number of outcomes favourable to E = 4

The number of possible outcomes = 52(Why ?)

Therefore,P(E) =

1

(ii)Let F be the event 'card drawn is not an ace".The number of outcomes favourable to the event F = 52 - 4 = 48(Why?)

302MATHEMATICS

t

Remark :

Note that F is nothing but

rth Example 5 : Two players, Sangeeta and Reshma, play a tennis match. It is known that the probability of Sangeeta winning the match is 0.62. What is the probability of

Reshma winning the match?

Solution : Let S and R denote the events that Sangeeta wins the match and Reshma wins the match, respectively. The probability of Sangeeta"s winning =P(S) = 0.62(given) The probability of Reshma"s winning =P(R) = 1 - P(S) [As the events R and S are complementary] =1 - 0.62 = 0.38 Example 6 : Savita and Hamida are friends. What is the probability that both will have (i)different birthdays?(ii)the same birthday? (ignoring a leap year). Solution : Out of the two friends, one girl, say, Savita"s birthday can be any day of the year. Now, Hamida"s birthday can also be any day of 365 days in the year. We assume that these 365 outcomes are equally likely. (i)If Hamida"s birthday is different from Savita"s, the number of favourable outcomes for her birthday is 365 - 1 = 364 So,P (Hamida"s birthday is different from Savita"s birthday) = r[Using P(

PROBABILITY303

vGlB.pkn€nnThere are 40 students in Class X of a school of whom 25 are girls and 15 are boys. The class teacher has to select one student as a class representative. She writes the name of each student on a separate card, the cards being identical. Then she puts cards in a bag and stirs them thoroughly. She then draws one card from the bag. What is the probability that the name written on the card is the name of (i) a girl? (ii) a boy? cpTb cCnnThere are 40 students, and only one name card has to be chosen.

(i)The number of all possible outcomes is 40The number of outcomes favourable for a card with the name of a girl = 25 (Why?)

Therefore, P (card with name of a girl) = P(Girl) = 1 1 cbknnWe can also determine P(Boy), by taking

P(Boy) =1 - P(not Boy) = 1 - P(Girl) =

P1 vGlB.pkn...nnA box contains 3 blue, 2 white, and 4 red marbles. If a marble is drawn at Pb -2A from the box, what is the probability that it will be (i)white?(ii)blue?(iii)red? cpTb cCnnSaying that a marble is drawn at random is a short way of saying that all the marbles are equally likely to be drawn. Therefore, the number of possible outcomes = 3 +2 + 4 = 9 (Why?) Let W denote the event 'the marble is white", B denote the event 'the marble is blue" and R denote the event 'marble is red". (i)The number of outcomes favourable to the event W = 2

So,P(W) =

296MATHEMATICS

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PROBABILITY305

t Example 11* : A missing helicopter is reported to have crashed somewhere in the rectangular region shown in Fig. 15.2. What is the probability that it crashed inside the lake shown in the figure?

Fig. 15.2

Solution : The helicopter is equally likely to crash anywhere in the region. Area of the entire region where the helicopter can crash =(4.5 × 9) km 2 = 40.5 km 2

296MATHEMATICS

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PROBABILITY307

‘ ‰enthe‡

Note that the pair (1, 4) is different from (4, 1). (Why?) So, the number of possible outcomes = 6 × 6 = 36. (i)The outcomes favourable to the event 'the sum of the two numbers is 8" denoted by E, are: (2, 6), (3, 5), (4, 4), (5, 3), (6, 2)quotesdbs_dbs20.pdfusesText_26

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